OpenRaiser/Intern-Atlas huggingface dataset:
An Evaluation Framework for Interactive Recommender System
Venue: arXiv cs.IR 2019
Year: 2019

Traditional recommender systems present a relatively static list of recommendations to a user where the feedback is typically limited to an accept/reject or a rating model. However, these simple modes of feedback may only provide limited insights as to why a user likes or dislikes an item and what aspects of the item the user has considered. Interactive recommender systems present an opportunity to engage the user in the process by allowing them to interact with the recommendations, provide feedback and impact the results in real-time. Evaluation of the impact of the user interaction typically requires an extensive user study which is time consuming and gives researchers limited opportunities to tune their solutions without having to conduct multiple rounds of user feedback. Additionally, user experience and design aspects can have a significant impact on the user feedback which may result in not necessarily assessing the quality of some of the underlying algorithmic decisions in the overall solution. As a result, we present an evaluation framework which aims to simulate the users interacting with the recommender. We formulate metrics to evaluate the quality of the interactive recommenders which are outputted by the framework once simulation is completed. While simulation along is not sufficient to evaluate a complete solution, the results can be useful to help researchers tune their solution before moving to the user study stage.
Simion Zoo: A Workbench for Distributed Experimentation with Reinforcement Learning for Continuous Control Tasks
Venue: arXiv cs.LG 2019
Year: 2019

We present Simion Zoo, a Reinforcement Learning (RL) workbench that provides a complete set of tools to design, run, and analyze the results,both statistically and visually, of RL control applications. The main features that set apart Simion Zoo from similar software packages are its easy-to-use GUI, its support for distributed execution including deployment over graphics processing units (GPUs) , and the possibility to explore concurrently the RL metaparameter space, which is key to successful RL experimentation.
An Efficient Formula Synthesis Method with Past Signal Temporal Logic
Venue: arXiv cs.LO 2019
Year: 2019

In this work, we propose a novel method to find temporal properties that lead to the unexpected behaviors from labeled dataset. We express these properties in past time Signal Temporal Logic (ptSTL). First, we present a novel approach for finding parameters of a template ptSTL formula, which extends the results on monotonicity based parameter synthesis. The proposed method optimizes a given monotone criteria while bounding an error. Then, we employ the parameter synthesis method in an iterative unguided formula synthesis framework. In particular, we combine optimized formulas iteratively to describe the causes of the labeled events while bounding the error. We illustrate the proposed framework on two examples.
ZoomOut: Spectral Upsampling for Efficient Shape Correspondence
Venue: arXiv cs.GR 2019
Year: 2019

We present a simple and efficient method for refining maps or correspondences by iterative upsampling in the spectral domain that can be implemented in a few lines of code. Our main observation is that high quality maps can be obtained even if the input correspondences are noisy or are encoded by a small number of coefficients in a spectral basis. We show how this approach can be used in conjunction with existing initialization techniques across a range of application scenarios, including symmetry detection, map refinement across complete shapes, non-rigid partial shape matching and function transfer. In each application we demonstrate an improvement with respect to both the quality of the results and the computational speed compared to the best competing methods, with up to two orders of magnitude speed-up in some applications. We also demonstrate that our method is both robust to noisy input and is scalable with respect to shape complexity. Finally, we present a theoretical justification for our approach, shedding light on structural properties of functional maps.
System-Level Simulator of LTE Sidelink C-V2X Communication for 5G
Venue: arXiv cs.NI 2019
Year: 2019

In recent years, Cellular-Vehicle-to-Everything (CV2X) has been an emerging area of interest attracting both the industry and academy societies to develop, which is also a prominent emerging service for the next generation of the cellular network (5G). In the time of the development, standardization, and further improvement of 5G, so simulations are essential to test and optimize algorithms and procedures prior to their implementation process of the equipment manufactures. And CV2X communication is used for information exchange among the traffic participants with network-assisted which can reduce traffic accidents and improve traffic efficiency. Moreover, it is also the primary enabler for cooperative driving. But CV2X communication has to meet different Quality of Service (QoS) requirements (e.g., ultra-high reliability (99.999%) and ultra-low latency). Guaranteeing high-level reliability is a big challenge. In order to assess system performance, accurate simulations of simple setups, as well as simulations of more complex systems via abstracted models are necessary for the CV2X communication. For checking the performance of the C-V2X communication on a highway scenario, a system-level simulator has been implemented. And, this simulation has been carried out on the network (system-level) context. Finally, the analysis and the simulation results for the C-V2X communication are presented, which shows that different objectives can be met via system-level simulation.
3D Shape Synthesis for Conceptual Design and Optimization Using Variational Autoencoders
Venue: arXiv cs.LG 2019
Year: 2019

We propose a data-driven 3D shape design method that can learn a generative model from a corpus of existing designs, and use this model to produce a wide range of new designs. The approach learns an encoding of the samples in the training corpus using an unsupervised variational autoencoder-decoder architecture, without the need for an explicit parametric representation of the original designs. To facilitate the generation of smooth final surfaces, we develop a 3D shape representation based on a distance transformation of the original 3D data, rather than using the commonly utilized binary voxel representation. Once established, the generator maps the latent space representations to the high-dimensional distance transformation fields, which are then automatically surfaced to produce 3D representations amenable to physics simulations or other objective function evaluation modules. We demonstrate our approach for the computational design of gliders that are optimized to attain prescribed performance scores. Our results show that when combined with genetic optimization, the proposed approach can generate a rich set of candidate concept designs that achieve prescribed functional goals, even when the original dataset has only a few or no solutions that achieve these goals.
Vehicle Detection and Classification using LTE-CommSense
Venue: arXiv cs.NI 2019
Year: 2019

We demonstrated a vehicle detection and classification method based on Long Term Evolution (LTE) communication infrastructure based environment sensing instrument, termed as LTE-CommSense by the authors. This technology is a novel passive sensing system which focuses on the reference signals embedded in the sub-frames of LTE resource grid. It compares the received signal with the expected reference signal, extracts the evaluated channel state information (CSI) and analyzes it to estimate the change in the environment. For vehicle detection and subsequent classification, our setup is similar to a passive radar in forward scattering radar (FSR) mode. Instead of performing the radio frequency (RF) signals directly, we take advantage of the processing that happens in a LTE receiver user equipment (UE). We tap into the channel estimation and equalization block and extract the CSI value. CSI value reflects the property of the communication channel between communication base station (eNodeB) and UE. We use CSI values for with and vehicle and without vehicle case in outdoor open road environment. Being a receiver only system, there is no need for any transmission and related regulations. Therefore, this system is low cost, power efficient and difficult to detect. Also, most of its processing will be done by the existing LTE communication receiver (UE). In this paper, we establish our claim by analyzing field-collected data. Live LTE downlink (DL) signal is captured using modeled LTE UE using software defined radio (SDR). The detection analysis and classification performance shows promising results and ascertains that, LTE-CommSense is capable of detection and classification of different types of vehicles in outdoor road environment.
Query Expansion for Cross-Language Question Re-Ranking
Venue: arXiv cs.IR 2019
Year: 2019

Community question-answering (CQA) platforms have become very popular forums for asking and answering questions daily. While these forums are rich repositories of community knowledge, they present challenges for finding relevant answers and similar questions, due to the open-ended nature of informal discussions. Further, if the platform allows questions and answers in multiple languages, we are faced with the additional challenge of matching cross-lingual information. In this work, we focus on the cross-language question re-ranking shared task, which aims to find existing questions that may be written in different languages. Our contribution is an exploration of query expansion techniques for this problem. We investigate expansions based on Word Embeddings, DBpedia concepts linking, and Hypernym, and show that they outperform existing state-of-the-art methods.
A QoS-Oriented Trajectory Optimization in Swarming Unmanned-Aerial-Vehicles Communications
Venue: arXiv cs.CE 2019
Year: 2019

This letter aims to present a novel approach for unmanned aerial vehicles (UAV)' path planning with respect to certain quality of service requirements. More specifically, we study the max-min fairness problem in an air-to-ground communication system where multiple UAVs and multiple ground stations exist. We jointly optimize the UAVs trajectories and power allocation as well as the user scheduling. To this end, we propose an effective iterative algorithm that relies on the successive convex approximation and the block coordinate decent techniques.
Machine learning for early prediction of circulatory failure in the intensive care unit
Venue: arXiv cs.LG 2019
Year: 2019

Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems. The limited ability of humans to process such complex information hinders physicians to readily recognize and act on early signs of patient deterioration. We used machine learning to develop an early warning system for circulatory failure based on a high-resolution ICU database with 240 patient years of data. This automatic system predicts 90.0% of circulatory failure events (prevalence 3.1%), with 81.8% identified more than two hours in advance, resulting in an area under the receiver operating characteristic curve of 94.0% and area under the precision-recall curve of 63.0%. The model was externally validated in a large independent patient cohort.
Metamorphic Testing for Quality Assurance of Protein Function Prediction Tools
Venue: arXiv cs.SE 2019
Year: 2019

Proteins are the workhorses of life and gaining insight on their functions is of paramount importance for applications such as drug design. However, the experimental validation of functions of proteins is highly-resource consuming. Therefore, recently, automated protein function prediction (AFP) using machine learning has gained significant interest. Many of these AFP tools are based on supervised learning models trained using existing gold-standard functional annotations, which are known to be incomplete. The main challenge associated with conducting systematic testing on AFP software is the lack of a test oracle, which determines passing or failing of a test case; unfortunately, due to the incompleteness of gold-standard data, the exact expected outcomes are not well defined for the AFP task. Thus, AFP tools face the \emph{oracle problem}. In this work, we use metamorphic testing (MT) to test nine state-of-the-art AFP tools by defining a set of metamorphic relations (MRs) that apply input transformations to protein sequences. According to our results, we observe that several AFP tools fail all the test cases causing concerns over the quality of their predictions.
Hard Sample Mining for the Improved Retraining of Automatic Speech Recognition
Venue: arXiv cs.SD 2019
Year: 2019

It is an effective way that improves the performance of the existing Automatic Speech Recognition (ASR) systems by retraining with more and more new training data in the target domain. Recently, Deep Neural Network (DNN) has become a successful model in the ASR field. In the training process of the DNN based methods, a back propagation of error between the transcription and the corresponding annotated text is used to update and optimize the parameters. Thus, the parameters are more influenced by the training samples with a big propagation error than the samples with a small one. In this paper, we define the samples with significant error as the hard samples and try to improve the performance of the ASR system by adding many of them. Unfortunately, the hard samples are sparse in the training data of the target domain, and manually label them is expensive. Therefore, we propose a hard samples mining method based on an enhanced deep multiple instance learning, which can find the hard samples from unlabeled training data by using a small subset of the dataset with manual labeling in the target domain. We applied our method to an End2End ASR task and obtained the best performance.
Neural Message Passing for Multi-Label Classification
Venue: arXiv cs.LG 2019
Year: 2019

Multi-label classification (MLC) is the task of assigning a set of target labels for a given sample. Modeling the combinatorial label interactions in MLC has been a long-haul challenge. We propose Label Message Passing (LaMP) Neural Networks to efficiently model the joint prediction of multiple labels. LaMP treats labels as nodes on a label-interaction graph and computes the hidden representation of each label node conditioned on the input using attention-based neural message passing. Attention enables LaMP to assign different importance to neighbor nodes per label, learning how labels interact (implicitly). The proposed models are simple, accurate, interpretable, structure-agnostic, and applicable for predicting dense labels since LaMP is incredibly parallelizable. We validate the benefits of LaMP on seven real-world MLC datasets, covering a broad spectrum of input/output types and outperforming the state-of-the-art results. Notably, LaMP enables intuitive interpretation of how classifying each label depends on the elements of a sample and at the same time rely on its interaction with other labels. We provide our code and datasets at https://github.com/QData/LaMP
DENet: A Universal Network for Counting Crowd with Varying Densities and Scales
Venue: arXiv cs.CV 2019
Year: 2019

Counting people or objects with significantly varying scales and densities has attracted much interest from the research community and yet it remains an open problem. In this paper, we propose a simple but an efficient and effective network, named DENet, which is composed of two components, i.e., a detection network (DNet) and an encoder-decoder estimation network (ENet). We first run DNet on an input image to detect and count individuals who can be segmented clearly. Then, ENet is utilized to estimate the density maps of the remaining areas, where the numbers of individuals cannot be detected. We propose a modified Xception as an encoder for feature extraction and a combination of dilated convolution and transposed convolution as a decoder. In the ShanghaiTech Part A, UCF and WorldExpo'10 datasets, our DENet achieves lower Mean Absolute Error (MAE) than those of the state-of-the-art methods.
DistanceNet: Estimating Traveled Distance from Monocular Images using a Recurrent Convolutional Neural Network
Venue: arXiv cs.CV 2019
Year: 2019

Classical monocular vSLAM/VO methods suffer from the scale ambiguity problem. Hybrid approaches solve this problem by adding deep learning methods, for example by using depth maps which are predicted by a CNN. We suggest that it is better to base scale estimation on estimating the traveled distance for a set of subsequent images. In this paper, we propose a novel end-to-end many-to-one traveled distance estimator. By using a deep recurrent convolutional neural network (RCNN), the traveled distance between the first and last image of a set of consecutive frames is estimated by our DistanceNet. Geometric features are learned in the CNN part of our model, which are subsequently used by the RNN to learn dynamics and temporal information. Moreover, we exploit the natural order of distances by using ordinal regression to predict the distance. The evaluation on the KITTI dataset shows that our approach outperforms current state-of-the-art deep learning pose estimators and classical mono vSLAM/VO methods in terms of distance prediction. Thus, our DistanceNet can be used as a component to solve the scale problem and help improve current and future classical mono vSLAM/VO methods.
A Lower Bound for Relaxed Locally Decodable Codes
Venue: arXiv cs.CC 2019
Year: 2019

A locally decodable code (LDC) C:{0,1}^k -> {0,1}^n is an error correcting code wherein individual bits of the message can be recovered by only querying a few bits of a noisy codeword. LDCs found a myriad of applications both in theory and in practice, ranging from probabilistically checkable proofs to distributed storage. However, despite nearly two decades of extensive study, the best known constructions of O(1)-query LDCs have super-polynomial blocklength. The notion of relaxed LDCs is a natural relaxation of LDCs, which aims to bypass the foregoing barrier by requiring local decoding of nearly all individual message bits, yet allowing decoding failure (but not error) on the rest. State of the art constructions of O(1)-query relaxed LDCs achieve blocklength n = O(k^{1+ γ}) for an arbitrarily small constant γ. We prove a lower bound which shows that O(1)-query relaxed LDCs cannot achieve blocklength n = k^{1+ o(1)}. This resolves an open problem raised by Goldreich in 2004.
Downlink Goodput Analysis for D2D Underlaying Massive MIMO Networks
Venue: arXiv cs.IT 2019
Year: 2019

The performance of downlink massive multiple-input-multiple-output networks with co-channel device-to-device communications is investigated in this paper. Specifically, we consider a cellular network with sufficient number of antennas at the base station and typical hexagonal cell coverage, where the cell users and device-to-device transmitters are randomly and uniformly distributed. To obtain the analytical expressions of system-level performance, the asymptotic signal-to-interference ratios for both downlink and device-to-device links are first obtained, which depend on the pathloss and small-scale fading of the interference channels. Since these information may not be available at the service base station or device-to-device transmitters, there exists a chance of packet outage. Therefore, we continue to derive the closed-form approximation of the average goodput, which measures the average number of information bits successfully delivered to the receiver. Hence, the system design trade-off between downlink and co-channel device-to-device communications can be investigated analytically. Moreover, the performance region in which the co-channel device-to-device communications could lead to better overall spectral efficiency can be obtained. Finally, it is shown by simulations that the analytical results matches the actual performance very well.
A Brief Note on Single Source Fault Tolerant Reachability
Venue: arXiv cs.DS 2019
Year: 2019

Let $G$ be a directed graph with $n$ vertices and $m$ edges, and let $s \in V(G)$ be a designated source vertex. We consider the problem of single source reachability (SSR) from $s$ in presence of failures of edges (or vertices). Formally, a spanning subgraph $H$ of $G$ is a {\em $k$-Fault Tolerant Reachability Subgraph ($k$-FTRS)} if it has the following property. For any set $F$ of at most $k$ edges (or vertices) in $G$, and for any vertex $v\in V(G)$, the vertex $v$ is reachable from $s$ in $G-F$ if and only if it is reachable from $s$ in $H - F$. Baswana et.al. [STOC 2016, SICOMP 2018] showed that in the setting above, for any positive integer $k$, we can compute a $k$-FTRS with $2^k n$ edges. In this paper, we give a much simpler algorithm for computing a $k$-FTRS, and observe that it extends to higher connectivity as well. Our results follow from a simple application of \emph{important separators}, a well known technique in Parameterized Complexity.
3D Object Recognition with Ensemble Learning --- A Study of Point Cloud-Based Deep Learning Models
Venue: arXiv cs.CV 2019
Year: 2019

In this study, we present an analysis of model-based ensemble learning for 3D point-cloud object classification and detection. An ensemble of multiple model instances is known to outperform a single model instance, but there is little study of the topic of ensemble learning for 3D point clouds. First, an ensemble of multiple model instances trained on the same part of the $\textit{ModelNet40}$ dataset was tested for seven deep learning, point cloud-based classification algorithms: $\textit{PointNet}$, $\textit{PointNet++}$, $\textit{SO-Net}$, $\textit{KCNet}$, $\textit{DeepSets}$, $\textit{DGCNN}$, and $\textit{PointCNN}$. Second, the ensemble of different architectures was tested. Results of our experiments show that the tested ensemble learning methods improve over state-of-the-art on the $\textit{ModelNet40}$ dataset, from $92.65\%$ to $93.64\%$ for the ensemble of single architecture instances, $94.03\%$ for two different architectures, and $94.15\%$ for five different architectures. We show that the ensemble of two models with different architectures can be as effective as the ensemble of 10 models with the same architecture. Third, a study on classic bagging i.e. with different subsets used for training multiple model instances) was tested and sources of ensemble accuracy growth were investigated for best-performing architecture, i.e. $\textit{SO-Net}$. We also investigate the ensemble learning of $\textit{Frustum PointNet}$ approach in the task of 3D object detection, increasing the average precision of 3D box detection on the $\textit{KITTI}$ dataset from $63.1\%$ to $66.5\%$ using only three model instances. We measure the inference time of all 3D classification architectures on a $\textit{Nvidia Jetson TX2}$, a common embedded computer for mobile robots, to allude to the use of these models in real-life applications.